Migration control apparatus and migration control method

ABSTRACT

A migration control apparatus includes a processor configured to, acquire a first communication log regarding a first virtual machine when execution of migration of the first virtual machine is not completed in a specific time, perform calculation of a first response time of a first processing request to the first virtual machine, acquire a second communication log regarding the first virtual machine after lowering frequency of assignment of a computing resource to the first virtual machine to a first rate, perform calculation of a second response time of a second processing request to the first virtual machine, calculate a second rate to which the frequency of assignment of the computing resource is permitted to be lowered, after starting re-execution of the migration, perform calculation of a third rate of the frequency of assignment of the computing resource for completing the re-execution, and perform determination of whether to cease the re-execution.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2018-137715, filed on Jul. 23,2018, the entire contents of which are incorporated herein by reference.

FIELD

The embodiments discussed herein are related to a migration controltechnique.

BACKGROUND

For example, a cloud computing user that provides services to usersconstructs and runs an information processing system for providingvarious services. For example, the cloud computing user constructs theinformation processing system by renting a virtual machine (VM) from acloud computing business operator that carries out lending of thevirtual machine.

In the above-described information processing system, for example, whena physical machine is desired to be stopped for carrying out maintenanceor the like, the cloud computing business operator carries out livemigration in which a virtual machine that is running is moved to anotherphysical machine without being stopped. Due to this, in the migrationcontrol system, it becomes possible to keep services provided to usersfrom being affected even when stop or the like of a physical machine iscarried out.

For example, related arts are disclosed in Japanese Laid-open PatentPublications No. 2015-156585, No. 2016-184252, and No. 2013-047920.

SUMMARY

According to an aspect of the embodiments, a migration control apparatusincludes a processor configured to, acquire a first communication logregarding a first virtual machine when execution of migration of thefirst virtual machine is not completed in a specific time, performcalculation of a first response time of a first processing request tothe first virtual machine, acquire a second communication log regardingthe first virtual machine after lowering frequency of assignment of acomputing resource to the first virtual machine to a first rate, performcalculation of a second response time of a second processing request tothe first virtual machine, calculate a second rate to which thefrequency of assignment of the computing resource is permitted to belowered, after start of re-execution of the migration, performcalculation of a third rate of the frequency of assignment of thecomputing resource for completing the re-execution, and performdetermination of whether to cease the re-execution.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram explaining a configuration of a migration controlsystem;

FIG. 2 is a diagram explaining a configuration of a migration controlsystem;

FIG. 3 is a diagram explaining a hardware configuration of a physicalmachine;

FIG. 4 is a block diagram of functions of a management VM;

FIG. 5 is a flowchart diagram explaining an outline of LM controlprocessing in a first embodiment;

FIG. 6 is a flowchart diagram explaining an outline of LM controlprocessing in the first embodiment;

FIG. 7 is a diagram explaining an outline of an LM control processing inthe first embodiment;

FIG. 8 is a diagram explaining an outline of an LM control processing inthe first embodiment;

FIG. 9 is a diagram explaining an outline of an LM control processing inthe first embodiment;

FIG. 10 is a flowchart diagram explaining details of an LM controlprocessing in the first embodiment;

FIG. 11 is a flowchart diagram explaining details of an LM controlprocessing in the first embodiment;

FIG. 12 is a flowchart diagram explaining details of an LM controlprocessing in the first embodiment;

FIG. 13 is a flowchart diagram explaining details of an LM controlprocessing in the first embodiment;

FIG. 14 is a flowchart diagram explaining details of an LM controlprocessing in the first embodiment;

FIG. 15 is a flowchart diagram explaining details of an LM controlprocessing in the first embodiment;

FIG. 16 is a diagram explaining a specific example of a planinformation;

FIG. 17 is a diagram explaining a specific example of a planinformation;

FIG. 18 is a diagram explaining a specific example of communicationlogs;

FIG. 19 is a diagram explaining a specific example of influenceprediction information;

FIG. 20 is a diagram explaining a specific example of LM logs;

FIG. 21 is a diagram explaining a specific example of LM logs;

FIG. 22 is a diagram explaining a specific example of LM logs;

FIG. 23 is a diagram explaining a specific example when a determinationresult output in processing of S55 is output to output apparatus; and

FIG. 24 is a diagram explaining a specific example when a determinationresult output in processing of S55 is output to output apparatus.

DESCRIPTION OF EMBODIMENTS

Here, in a virtual machine of a live migration target, data is updatedeven in execution of the live migration. For this reason, in the livemigration, if data that has been transferred to another physical machineis updated, retransfer about the data is carried out. Therefore, in theinformation processing system, there is a possibility that the livemigration is not completed in a desired time (for example, in a timedefined in advance by the cloud computing business operator) if thefrequency of update of data in execution of the live migration is high.

Thus, the cloud computing business operator carries out slowdown of avirtual machine by lowering the frequency of assignment of a centralprocessing unit (CPU) to the virtual machine of the live migrationtarget, for example. This allows the cloud computing business operatorto suppress the frequency of update of data in the virtual machine ofthe live migration target.

However, in the case of carrying out the slowdown of the virtual machineof the live migration target, the execution speed of processing ofproviding services to users becomes lower in the virtual machine of thelive migration target. For this reason, there is a possibility that theexecution of the live migration affects the services for users in theinformation processing system.

First, the configuration of a migration control system 10 will bedescribed. FIG. 1 and FIG. 2 are diagrams explaining the configurationof the migration control system 10. The migration control system 10illustrated in FIG. 1 and FIG. 2 includes physical machines 1 a, 1 b,and 1 c and an operation terminal 2. The physical machines 1 a, 1 b, and1 c are one example of migration control apparatus.

For example, the operation terminal 2 is a personal computer (PC) usedby a cloud computing user and is a terminal that accesses the physicalmachines 1 a, 1 b, and 1 c through a network NW such as the Internet.

The physical machines 1 a, 1 b, and 1 c include pieces of hardware 5 a,5 b, and 5 c, respectively, including CPU, memory (dynamic random accessmemory (DRAM)), hard disk (hard disk drive (HDD)), network, and soforth. Furthermore, in the physical machines 1 a, 1 b, and 1 c, piecesof virtualization software 4 a, 4 b, and 4 c, respectively, operate onthe pieces of hardware 5 a, 5 b, and 5 c as illustrated in FIG. 1.

The virtualization software 4 a executes processing of generating VMs 3a, 3 b, and 3 c by assigning part of the hardware 5 a, for example.Furthermore, the virtualization software 4 b executes processing ofgenerating VMs 3 d and 3 e by assigning part of the hardware 5 b, forexample. In addition, each of the VMs 3 a, 3 b, 3 c, 3 d, and 3 eexecutes processing of providing services to users, for example.

Moreover, the virtualization software 4 c executes processing ofgenerating a VM 3 f (hereinafter, referred to also as management VM 3 f)by assigning part of the hardware 5 c. Furthermore, the management VM 3f executes processing of controlling live migration and so forth of theVMs 3 a, 3 b, 3 c, 3 d, and 3 e, for example. For example, asillustrated in FIG. 2, when detecting that the processing load of thephysical machine 1 a has become higher than a given threshold, themanagement VM 3 f migrates the VM 3 c that is operating on the physicalmachine 1 a to the physical machine 1 b by making an instruction to thevirtualization software 4 a.

Hereinafter, the physical machines 1 a, 1 b, and 1 c will becollectively referred to also as the physical machine 1 simply. The VMs3 a, 3 b, 3 c, 3 d, 3 e, and 3 f will be collectively referred to alsoas the VM 3 simply. The pieces of virtualization software 4 a, 4 b, and4 c will be collectively referred to also as the virtualization software4 simply. The pieces of hardware 5 a, 5 b, and 5 c will be collectivelyreferred to also as the hardware 5 simply.

Furthermore, hereinafter, the description will be made based on theassumption that management of the VMs 3 a, 3 b, 3 c, 3 d, and 3 e iscarried out by the cloud computing user and management of thevirtualization software 4, the hardware 5, and the management VM 3 f iscarried out by the cloud computing business operator. For example,hereinafter, the description will be made based on the assumption thatit is difficult for the cloud computing business operator to acquirevarious kinds of information by directly accessing the VMs 3 a, 3 b, 3c, 3 d, and 3 e.

Here, in the VM 3 of the live migration target, data is updated even inexecution of the live migration. For this reason, in the live migrationof the VM 3, if data that has been already transferred to anotherphysical machine 1 is updated, retransfer is desired to be carried outabout the updated data. Therefore, in the migration control system 10,there is a possibility that the completion time of the live migrationbecomes longer than the initial predicted time if the frequency ofupdate of data in execution of the live migration is high.

Thus, in this case, for example, the cloud computing user carries outslowdown of the VM 3 (processing speed of the VM 3) by lowering thefrequency of assignment (time of assignment) of the CPU to the VM 3 ofthe live migration target. This allows the cloud computing user tosuppress the frequency of update of data carried out in the VM 3 of thelive migration target.

However, in the case of carrying out the slowdown of the VM 3 of thelive migration target, the execution speed of processing of providingservices to users becomes lower in the VM 3 of the live migrationtarget. For this reason, there is a possibility that the execution ofthe live migration affects the services for users in the migrationcontrol system 10.

Thus, if execution of live migration of the VM 3 of the live migrationtarget (hereinafter, referred to also as target VM 3) is not completedin a given time, the management VM 3 f in the present embodimentacquires communication logs about the target VM 3 (hereinafter, referredto also as first communication logs). Then, the management VM 3 fcalculates the response time of a processing request to the target VM 3(hereinafter, referred to also as first response time) based on theacquired first communication logs.

Subsequently, the management VM 3 f lowers the frequency of assignmentof the CPU to the target VM 3 and thereafter acquires communication logsabout the target VM 3 (hereinafter, referred to also as secondcommunication logs). Then, the management VM 3 f calculates a secondresponse time of a processing request to the target VM 3 based on theacquired second communication logs.

Thereafter, the management VM 3 f calculates the rate by which thefrequency of assignment may be lowered (hereinafter, referred to also asfirst rate) based on the rate by which the frequency of assignment hasbeen lowered, the first response time, the second response time, and thetime permitted as the response time of a processing request to thetarget VM 3. Then, after start of re-execution of the live migration ofthe target VM 3, the management VM 3 f calculates the rate by which thefrequency of assignment is desired to be lowered for completing there-execution (hereinafter, referred to also as second rate) based on theprogress status of the re-execution. Moreover, the management VM 3 fdetermines whether or not to cease the re-execution of the livemigration based on the first rate and the second rate.

For example, the management VM 3 f carries out slowdown of the target VM3 in a range in which the response time with respect to a processingrequest from a user does not exceed the permitted time and suppressesthe frequency of update of data in execution of the live migration ofthe target VM 3.

This allows the management VM 3 f to complete the live migration of thetarget VM 3 while keeping the services provided to users by the cloudcomputing user from being affected. For example, it becomes possible forthe management VM 3 f to complete the live migration of the target VM 3while suppressing the occurrence of an error and so forth associatedwith delay in the response time of a processing request from a user tothe target VM 3, for example.

Next, the hardware configuration of the migration control system 10 willbe described. FIG. 3 is a diagram explaining the hardware configurationof the physical machine 1.

As illustrated in FIG. 3, the physical machine 1 includes a CPU 101 thatis a processor, a memory 102, an external interface (I/O unit) 103, anda storage medium 104. The respective units are coupled to each otherthrough a bus 105.

The storage medium 104 includes a program storage area (not illustrated)that stores a program 110 for executing processing of controlling livemigration of the target VM 3 (hereinafter, referred to also as livemigration control processing or LM control processing), for example.Furthermore, the storage medium 104 includes a storing unit 130(hereinafter, referred to also as information storage area 130) thatstores information used when the LM control processing is executed, forexample. The storage medium 104 may be an HDD, for example.

The CPU 101 executes the program 110 loaded from the storage medium 104into the memory 102 to execute the LM control processing.

Furthermore, the external interface 103 carries out communication withthe operation terminal 2, for example.

Next, functions of the migration control system 10 will be described.FIG. 4 is a block diagram of functions of the management VM 3 f.

As illustrated in FIG. 4, through organic cooperation between pieces ofhardware such as the CPU 101 and the memory 102 of the physical machine1 c and the program 110, the management VM 3 f implements variousfunctions including an information management unit 111, a migrationexecuting unit 112, a log acquiring unit 113, an influence inferringunit 114, a processing identifying unit 115, a slowdown executing unit116, an LM monitoring unit 117, and an LM predicting unit 118.

Furthermore, the management VM 3 f stores plan information 131,communication logs 132, an influence prediction information 133, and LMlogs 134 in the information storage area 130 as illustrated in FIG. 4.

The information management unit 111 stores the plan information 131 inthe information storage area 130. The plan information 131 isinformation that represents an execution plan of live migration of thetarget VM 3 and is information generated by a cloud computing businessoperator in advance, for example.

The migration executing unit 112 executes live migration of the targetVM 3 in accordance with the plan information 131 stored in theinformation storage area 130.

For example, if the migration executing unit 112 detects that theexecution of the live migration of the target VM 3 is not completed in agiven time, the log acquiring unit 113 acquires the first communicationlogs 132 about the target VM 3.

The influence inferring unit 114 calculates the response time of aprocessing request to the target VM 3 (hereinafter, referred to also asfirst response time) based on the first communication logs 132 acquiredby the log acquiring unit 113.

The processing identifying unit 115 determines whether or not the targetVM 3 is the VM 3 that executes processing for a processing requesttransmitted from the operation terminal 2 based on the firstcommunication logs 132 acquired by the log acquiring unit 113.

The slowdown executing unit 116 carries out slowdown of the target VM 3by lowering the frequency of assignment of the CPU (CPU of the physicalmachine 1 on which the target VM 3 is generated) to the target VM 3. Forexample, if it is determined by the processing identifying unit 115 thatthe target VM 3 is the VM 3 that executes processing for a processingrequest, the slowdown executing unit 116 instructs the virtualizationsoftware 4 to carry out slowdown of the target VM 3. The CPU assigned tothe target VM 3 may be a virtual central processing unit (VCPU) formedof at least part of the CPU 101 explained with FIG. 3, for example.

Furthermore, after the slowdown executing unit 116 lowers the frequencyof assignment of the CPU to the target VM 3, the log acquiring unit 113acquires the second communication logs 132 about the target VM 3. Then,the influence inferring unit 114 calculates the response time of aprocessing request to the target VM 3 (hereinafter, referred to also assecond response time) based on the second communication logs 132acquired by the log acquiring unit 113.

Thereafter, the influence inferring unit 114 calculates the first rateby which the frequency of assignment of the CPU to the target VM 3 maybe lowered based on the rate by which the frequency of assignment of theCPU has been lowered by the slowdown executing unit 116, the firstresponse time, the second response time, and the time permitted as theresponse time of a processing request to the target VM 3.

After the migration executing unit 112 starts re-execution of the livemigration of the target VM 3, the LM monitoring unit 117 calculates thesecond rate by which the frequency of assignment of the CPU is desiredto be lowered for completing the re-execution of the live migrationbased on the progress status of the re-execution of the live migration.

The LM predicting unit 118 determines whether or not to cease there-execution of the live migration based on the first rate calculated bythe influence inferring unit 114 and the second rate calculated by theLM monitoring unit 117. A description about specific examples of theinfluence prediction information 133 and the LM logs 134 will be madelater.

Next, the outline of a first embodiment will be described. FIG. 5 andFIG. 6 are flowchart diagrams explaining an outline of LM controlprocessing in the first embodiment. Furthermore, FIG. 7 to FIG. 9 arediagrams explaining an outline of LM control processing in the firstembodiment.

As illustrated in FIG. 5, the management VM 3 f waits until detectingthe target VM 3 about which execution of live migration is not completedin a given time (NO of S1).

Then, when detecting the target VM 3 about which execution of livemigration is not completed in the given time (YES of S1), the managementVM 3 f acquires the first communication logs 132 about the target VM 3(S2). The given time is a time defined in advance by a cloud computingbusiness operator as the completion predicted time of the livemigration, for example. For example, the given time may be a time commonabout live migration of all VMs 3 or be a time different for each oflive migrations of the respective VMs 3.

Subsequently, the management VM 3 f calculates the first response timeof a processing request to the target VM 3 based on the firstcommunication logs 132 acquired in the processing of S2 (S3).

For example, as illustrated in FIG. 7, for example, a management VM 3 facquires the first communication logs 132 about the VM 3 c whendetecting that live migration of the VM 3 c from the physical machine 1a to the physical machine 1 b is not completed in the given time. Then,the management VM 3 f refers to the first communication logs 132 aboutthe VM 3 c and calculates the first response time of a processingrequest to the target VM 3.

For example, the VM 3 c illustrated in FIG. 7 is the VM 3 managed by acloud computing user. For this reason, it is difficult for themanagement VM 3 f managed by the cloud computing business operator toacquire various kinds of information by directly accessing the VM 3 c.On the other hand, the VM 3 c goes through a virtual switch (notillustrated) and so forth generated by the virtualization software 4 awhen carrying out communication with the operation terminal 2 or thelike. Thus, the management VM 3 f acquires the first communication logs132 about the VM 3 c by accessing the virtual switch generated by thevirtualization software 4 a. Then, the management VM 3 f calculates thefirst response time from the acquired first communication logs 132.

Subsequently, the management VM 3 f acquires the second communicationlogs 132 about the target VM 3 after lowering the frequency ofassignment of the CPU to the target VM 3 (S4). Then, the management VM 3f calculates the second response time of a processing request to thetarget VM 3 based on the second communication logs 132 acquired in theprocessing of S4 (S5).

For example, as illustrated in FIG. 8, for example, the management VM 3f acquires the second communication logs 132 about the VM 3 c afterlowering the frequency of assignment of the CPU to the VM 3 c. Then, themanagement VM 3 f refers to the second communication logs 132 about theVM 3 c and calculates the second response time of a processing requestto the target VM 3.

Thereafter, as illustrated in FIG. 6, the management VM 3 f calculatesthe first rate by which the frequency of assignment of the CPU to thetarget VM 3 may be lowered based on the rate by which the frequency ofassignment has been lowered in the processing of S4, the first responsetime calculated in the processing of S3, the second response timecalculated in the processing of S5, and the time permitted as theresponse time of a processing request to the target VM 3 (S11).

For example, the management VM 3 f calculates, as the first rate, thelowering rate with which it may be determined that the services to usersare not affected in the case of lowering the frequency of assignment ofthe CPU to the target VM 3.

Then, after start of re-execution of the live migration of the target VM3, the management VM 3 f calculates the second rate by which thefrequency of assignment is desired to be lowered for completing there-execution of the live migration based on the progress status of there-execution of the live migration (S12).

For example, the management VM 3 f calculates the lowering rate of thefrequency of assignment of the CPU desired for completing the livemigration of the target VM 3.

Moreover, the management VM 3 f determines whether or not to cease there-execution of the live migration of the target VM 3 based on the firstrate calculated in the processing of S11 and the second rate calculatedin the processing of S12 (S13).

For example, as illustrated in FIG. 9, the management VM 3 f calculateseach of the first rate and the second rate about the VM 3 from the firstresponse time and the second response time calculated about the VM 3.Then, for example, if the second rate is higher than the first rate, themanagement VM 3 f determines to cease the live migration of the VM 3.

This allows the management VM 3 f to complete the live migration of thetarget VM 3 while keeping the services provided to users by the cloudcomputing user from being affected. For example, it becomes possible forthe management VM 3 f to complete the live migration of the target VM 3while suppressing the occurrence of an error and so forth associatedwith delay in the response time of a processing request from a user tothe target VM 3, for example.

Next, details of the first embodiment will be described. FIG. 10 to FIG.15 are flowchart diagrams explaining details of LM control processing inthe first embodiment. Furthermore, FIG. 16 to FIG. 24 are diagramsexplaining details of LM control processing in the first embodiment.

As illustrated in FIG. 10, the migration executing unit 112 of themanagement VM 3 f waits until an LM execution timing comes (NO of S21).The LM execution timing is the timing when live migration of the VM 3 isstarted and is the timing about which information is included in theplan information 131 stored in the information storage area 130.Specific examples of the plan information 131 will be described below.

FIG. 16 and FIG. 17 are diagrams explaining the specific examples of theplan information 131. The plan information 131 illustrated in FIG. 16and so forth includes, as items, “migration source PM” in whichidentification information of the physical machine 1 of the migrationsource of live migration is stored, “target VM” in which identificationinformation of the target VM 3 of live migration is stored, and“migration destination PM” in which identification information of thephysical machine 1 of the migration destination of live migration isstored. Furthermore, the plan information 131 illustrated in FIG. 16 andso forth includes, as items, “start clock time (scheduled)” in which thescheduled start clock time of live migration is stored and “executiontime (scheduled)” in which the scheduled execution time of livemigration is stored. Moreover, the plan information 131 illustrated inFIG. 16 and so forth includes, as items, “start clock time” in which theactual start clock time of live migration is stored, “execution time” inwhich the actual execution time of live migration is stored, and“result” in which the execution result of live migration (whether or notlive migration has been completed) is stored.

For example, in the information on the first row of the plan information131 illustrated in FIG. 16, “PM1” is stored as “migration source PM” and“ABC” is stored as “target VM” and “PM2” is stored as “migrationdestination PM.” Furthermore, “9:00” is stored as “start clock time(scheduled)” and “20 (minutes)” is stored as “execution time(scheduled).” In addition, in the information on the first row of theplan information 131 illustrated in FIG. 16, “−” indicating thatinformation is not stored is stored in each of “start clock time,”“execution time,” and “result.”

Furthermore, in the information on the second row of the planinformation 131 illustrated in FIG. 16, “PM1” is stored as “migrationsource PM” and “DEF” is stored as “target VM” and “PM3” is stored as“migration destination PM.” Moreover, “9:20” is stored as “start clocktime (scheduled)” and “40 (minutes)” is stored as “execution time(scheduled).” In addition, in the information on the second row of theplan information 131 illustrated in FIG. 16, “−” is stored in each of“start clock time,” “execution time,” and “result.” Explanation of theother pieces of information included in FIG. 16 is omitted.

Referring back to FIG. 10, when the LM execution timing comes (YES ofS21), the migration executing unit 112 starts execution of livemigration of the target VM 3 (S22).

The migration executing unit 112 may store the execution result of thelive migration for which execution has been started in the processing ofS22 in the plan information 131. A specific example of the planinformation 131 when the execution result of live migration is storedwill be described below.

FIG. 17 is a diagram explaining the specific example of the planinformation 131 when the execution result of live migration is stored.For example, in the information on the first row of the plan information131 illustrated in FIG. 17, “9:00” is stored as “start clock time” and“15 (minutes)” is stored as “execution time” and “success” is stored as“result.”

Furthermore, in the information on the second row of the planinformation 131 illustrated in FIG. 17, “9:15” is stored as “start clocktime” and “60 (minutes)” is stored as “execution time” and “failure” isstored as “result.”

For example, the information on the first row of the plan information131 illustrated in FIG. 17 represents that live migration of the targetVM 3 whose identification information is “ABC” has been completed in 60minutes (live migration has succeeded without the occurrence oftimeout). Furthermore, the information on the second row of the planinformation 131 illustrated in FIG. 17 represents that live migration ofthe target VM 3 whose identification information is “DEF” has not beencompleted even after the elapse of 60 minutes (live migration has faileddue to the occurrence of timeout). Explanation of the other pieces ofinformation included in FIG. 17 is omitted.

Referring back to FIG. 10, if the existence of the target VM 3 aboutwhich timeout has occurred in the execution of the live migration is notdetected (NO of S23), the management VM 3 f ends the LM controlprocessing.

For example, in this case, the management VM 3 f determines that all oflive migrations of the respective target VMs 3 have been completedwithout timeout, and ends the LM control processing.

On the other hand, if the existence of the target VM 3 about whichtimeout has occurred in the execution of the live migration is detected(YES of S23), the management VM 3 f determines whether or not the causeof the timeout that has occurred about the target VM 3 existing in theprocessing of S23 is bandwidth insufficiency of the network NW (S24).

For example, the management VM 3 f acquires the traffic amount in aphysical switch (not illustrated) deployed in the network NW, forexample. Then, for example, the management VM 3 f determines that thecause of the timeout that has occurred about the target VM 3 isbandwidth insufficiency of the network NW if the sum of the bandwidthfor communication in the acquired traffic amount and the bandwidth fortransfer of data associated with the live migration of the target VM 3is larger than the bandwidth of the network NW.

Then, if it is determined that the cause of the timeout that hasoccurred about the target VM 3 is bandwidth insufficiency of the networkNW (YES of S25), for example, the migration executing unit 112 outputsinformation indicating that timeout of live migration has occurred dueto bandwidth insufficiency of the network NW to output apparatus (notillustrated) (S26). Then, the management VM 3 f ends the LM controlprocessing.

On the other hand, if it is determined that the cause of the timeoutthat has occurred about the target VM 3 is not bandwidth insufficiencyof the network NW (NO of S25), as illustrated in FIG. 11, the logacquiring unit 113 of the management VM 3 f starts acquisition of thecommunication logs 132 about the target VM 3 existing in the processingof S23 (S31).

Then, the processing identifying unit 115 of the management VM 3 frefers to the communication logs 132 (first communication logs 132)about the target VM 3 existing in the processing of S23 and determineswhether or not the target VM 3 existing in the processing of S23 is theVM 3 that executes processing for a processing request (S32). A specificexample of the communication logs 132 will be described below.

FIG. 18 is a diagram explaining the specific example of thecommunication logs 132. On the first row of the communication logs 132illustrated in FIG. 18, “11:38:37.060846 IP client2.63396 >host1.8081:Flags [S], seq 3907359389, win 8192, options [mss 1460,nop,wscale8,nop,nop,sackOK], length 0” is stored. Furthermore, on the second rowof the communication logs 132 illustrated in FIG. 18, “11:38:37.078862IP client2.63396 >host1.8081: Flags [F.], seq 358, ack 21513, win 256,length 0” is stored. Explanation of the other pieces of informationincluded in FIG. 18 is omitted.

Then, for example, the processing identifying unit 115 identifies thecommunication log 132 in which the transmission direction of the packetis the target VM 3 in the communication logs 132 illustrated in FIG. 18and determines whether or not the TCP port number of the identifiedcommunication log 132 is smaller than “49152.”

For example, on the first row of the communication logs 132 illustratedin FIG. 18, “>host1.8081” indicating that the host name of thetransmission direction of the packet is host1 and the TCP port number is8081 is included. Thus, for example, if the host name of the target VM 3existing in the processing of S23 is host1, the processing identifyingunit 115 determines that the target VM 3 existing in the processing ofS23 is the VM 3 that executes processing for a processing request.

Referring back to FIG. 11, if it is determined that the target VM 3existing in the processing of S23 is not the VM 3 that executesprocessing for a processing request (NO of S33), the migration executingunit 112 stops the target VM 3 existing in the processing of S23 (S34).Then, the migration executing unit 112 carries out migration of thetarget VM 3 stopped in the processing of S34 (S35). Thereafter, themanagement VM 3 f ends the LM control processing.

For example, if the target VM 3 existing in the processing of S23 is notthe VM 3 that executes processing for a processing request, the cloudcomputing business operator may determine that the influence on theservices for users is small although the target VM 3 is stopped. Forthis reason, in this case, the migration executing unit 112 carries outmigration involving the stop of the target VM 3 instead of livemigration.

On the other hand, if it is determined that the target VM 3 existing inthe processing of S23 is the VM 3 that executes processing for aprocessing request (YES of S33), as illustrated in FIG. 12, theinfluence inferring unit 114 refers to the communication logs 132 aboutthe target VM 3 existing in the processing of S23 and calculates theresponse time (first response time) of a processing request to thetarget VM 3 existing in the processing of S23 (S41).

For example, in this case, the influence inferring unit 114 calculatesthe response time in the target VM 3 before slowdown is carried out.

For example, the influence inferring unit 114 identifies, from thecommunication logs 132 illustrated in FIG. 18, the communication log 132including “client2.63396” indicating that the host name of thetransmission source of the packet is client2 and the TCP port number is63396 and “S” that is a flag indicating the start of a session (forexample, communication log 132 on the first row). Furthermore, theinfluence inferring unit 114 identifies the communication log 132including “client2.63396” and “F” that is a flag indicating the end of asession (for example, communication log 132 on the second row) from thecommunication logs 132 illustrated in FIG. 18, for example.

Then, the influence inferring unit 114 identifies “11:38:37.060846” and“11:38:37.078862” that are the clock times included in the respectiveidentified communication logs 132 and works out, as the response time,“about 18 (ms)” that is the time obtained by subtracting“11:38:37.060846” from “11:38:37.078862”, for example. If pluralresponse times are calculated from the communication logs 132, theinfluence inferring unit 114 may calculate the average of the calculatedresponse times.

Referring back to FIG. 12, the slowdown executing unit 116 of themanagement VM 3 f carries out slowdown of the target VM 3 by loweringthe frequency of assignment of the CPU to the target VM 3 existing inthe processing of S23 (S42). For example, the slowdown executing unit116 lowers the frequency of assignment of the CPU to the target VM 3existing in the processing of S23 by 10 (%), for example.

Then, the influence inferring unit 114 refers to the communication logs132 (second communication logs 132) after the slowdown about the targetVM 3 existing in the processing of S23 and calculates the response time(second response time) of a processing request to the target VM 3existing in the processing of S23 (S43).

For example, in this case, the influence inferring unit 114 calculatesthe response time in the target VM 3 after the slowdown is carried out.

Subsequently, the log acquiring unit 113 ends the acquisition of thecommunication logs 132 about the target VM 3 existing in the processingof S23 (S44).

Then, the influence inferring unit 114 generates the influenceprediction information 133 from the response time calculated in theprocessing of S41 and the response time calculated in the processing ofS43 (S45). The influence prediction information 133 is information thatrepresents change in the response time according to the execution of theslowdown of the target VM 3. Thereafter, the information management unit111 stores the influence prediction information 133 generated by theinfluence inferring unit 114 in the information storage area 130, forexample. A specific example of the influence prediction information 133will be described below.

FIG. 19 is a diagram explaining the specific example of the influenceprediction information 133. The influence prediction information 133illustrated in FIG. 19 includes, as items, “slowdown rate” in which thelowering rate of the frequency of assignment of the CPU is stored and“response time” in which the response time with respect to a processingrequest transmitted from the operation terminal 2 is set.

For example, in the information on the first row of the influenceprediction information 133 illustrated in FIG. 19, “0 (%)” is stored as“slowdown rate” and “18 (ms)” is stored as “response time.”

Furthermore, in the information on the second row of the influenceprediction information 133 illustrated in FIG. 19, “10 (%)” is stored as“slowdown rate” and “20 (ms)” is stored as “response time.”

Referring back to FIG. 12, the influence inferring unit 114 calculatesthe rate (first rate) by which the frequency of assignment of the CPU tothe target VM 3 existing in the processing of S23 may be lowered fromthe rate by which the frequency of assignment of the CPU has beenlowered in the processing of S42, the influence prediction information133 generated in the processing of S45, and the permitted value of theresponse time (hereinafter, referred to also as response permitted time)with respect to a processing request transmitted to the target VM 3existing in the processing of S23 (S46). The response permitted time maybe decided by the cloud computing business operator in advance, forexample.

For example, the influence inferring unit 114 calculates the rate bywhich the frequency of assignment of the CPU to the target VM 3 existingin the processing of S23 may be lowered by using the followingexpression (1), for example.

Response time after slowdown=coefficient×response time beforeslowdown/(1−lowering rate of frequency of assignment)   expression (1)

First, the influence inferring unit 114 calculates “coefficient” in theabove-described expression (1). For example, in the influence predictioninformation 133 explained with FIG. 19, the time corresponding to“response time before slowdown” is “18 (ms)” and the time correspondingto “response time after slowdown” is “20 (ms).” Thus, when “loweringrate of frequency of assignment” in the processing of S42 is “10 (%),”the influence inferring unit 114 works out “1” as “coefficient.”

Next, by using the above-described expression (1), the influenceinferring unit 114 calculates “lowering rate of frequency of assignment”when “response time after slowdown” is the response permitted time and“response time before slowdown” is “18 (ms)” and “coefficient” is “1.”For example, when the response permitted time is “300 (ms),” theinfluence inferring unit 114 works out “94 (%)” as “lowering rate offrequency of assignment.”

For example, in this case, it is possible for the management VM 3 f todetermine that “lowering rate of frequency of assignment” is desired tobe suppressed to “94 (%)” or lower in order to suppress “response timeafter slowdown” to “300 (ms)” or shorter.

Thereafter, as illustrated in FIG. 13, the migration executing unit 112starts re-execution of the live migration of the target VM 3 existing inthe processing of S23 (S51).

Then, the LM monitoring unit 117 of the management VM 3 f acquires theLM logs 134 about the target VM 3 for which the re-execution has beenstarted in the processing of S51 (S52). The LM logs 134 are logs thatrepresent the progress status of the live migration of the target VM 3.For example, the LM monitoring unit 117 accesses the physical machine 1on which the target VM 3 for which the re-execution has been started inthe processing of S51 operates and acquires the desired LM logs 134, forexample. Then, the information management unit 111 stores the LM logs134 acquired by the LM monitoring unit 117 in the information storagearea 130, for example. Specific examples of the LM logs 134 will bedescribed below.

FIG. 20 to FIG. 22 are diagrams explaining the specific examples of theLM logs 134. The LM logs 134 illustrated in FIG. 20 and so forth have,as items, “clock time” in which the generation clock time of each LM log134 is stored, “status” in which the execution status about livemigration of the target VM 3 is stored, and “lowering rate of frequencyof assignment” in which the rate by which the frequency of assignment ofthe CPU to the target VM 3 has been lowered is stored. Furthermore, theLM logs 134 illustrated in FIG. 20 and so forth have, as items,“transferred data” in which the data amount of data transferred inassociation with execution of live migration of the target VM 3 isstored and “generated dirty page” in which the data amount of pagesincluding data updated in transfer of data associated with execution oflive migration of the target VM 3 (hereinafter, referred to also asdirty pages) is stored.

The migration executing unit 112 repeatedly carries out transfer of thedirty pages until the transfer time of the dirty pages generated at thetime of the previous transfer becomes a given time or shorter. Thus, thenumber of times of transfer of the dirty pages is set in “status” in theLM logs 134, for example. Furthermore, when the transfer time of thedirty pages generated at the time of the previous transfer has becomethe given time or shorter, the migration executing unit 112 stops thetarget VM 3 and transfers the dirty pages that have not beentransferred.

Moreover, in terms of enhancing the probability of completion of livemigration, the slowdown executing unit 116 increases the lowering rateof the frequency of assignment by a given rate every time the number oftimes of transfer of the dirty pages increases. In the following, thedescription will be made based on the assumption that the slowdownexecuting unit 116 increases the lowering rate of the frequency ofassignment by 10 (%) in every increase.

For example, on the first row of the LM logs 134 illustrated in FIG. 20,“10:11:00” is stored as “clock time” and “LM start” indicating the startof live migration is stored as “status.” In addition, “10 (%)” is storedas “lowering rate of frequency of assignment” and “−” is stored as“transferred data” and “generated dirty page.”

Furthermore, on the second row of the LM logs 134 illustrated in FIG.20, “10:11:32” is stored as “clock time” and “Iter 1” indicating thefirst round of transfer is stored as “status.” In addition, “20 (%)” isstored as “lowering rate of frequency of assignment” and “32000 (MiB)”is set as “transferred data” and “17920 (MiB)” is set as “generateddirty page.”

For example, the LM logs 134 illustrated in FIG. 20 represent that thedata amount of all pieces of data transferred in association withre-execution of live migration of the target VM 3 is “32000 (MiB)” anddirty pages with a data amount of “17920 (MiB)” have been generated inthe transfer of the data.

Referring back to FIG. 13, the LM predicting unit 118 of the managementVM 3 f refers to the LM logs 134 acquired in the processing of S52 andinfers the rate (second rate) by which the frequency of assignment ofthe CPU is desired to be lowered for completing the re-execution startedin the processing of S51 (S53). Details of the processing of S53 will bedescribed below.

FIG. 14 and FIG. 15 are flowchart diagrams explaining the details of theprocessing of S53. As illustrated in FIG. 14, after the processing ofS52, the LM predicting unit 118 waits until transfer (first round oftransfer) of all pieces of data desired to be transferred in associationwith the execution of the live migration of the target VM 3 is completed(NO of S101).

Then, when the transfer of all pieces of data desired to be transferredin association with the execution of the live migration of the target VM3 is completed (YES of S101), the LM predicting unit 118 identifiesdirty pages including data updated after the transfer in the pieces ofdata for which the transfer has been completed in the processing of S101(S102).

For example, the LM predicting unit 118 refers to the LM logs 134explained with FIG. 20 and identifies “17920 (MiB)” set in “generateddirty page” of the information whose “status” is “Iter 1.”

Subsequently, the LM predicting unit 118 divides the data amount of thedirty pages identified in the processing of S102 by the time taken forthe transfer of the data for which the transfer has been completed inthe processing of S101 and the present frequency of assignment of theCPU to the target VM 3 for which the re-execution has been started inthe processing of S51 to calculate the generation frequency of the dirtypages in the case in which the frequency of assignment of the CPU hasnot been lowered (S103).

For example, on the first row and the second row of the LM logs 134explained with FIG. 20, “10:11:00” and “10:11:32,” respectively, arestored as “clock time.” Furthermore, on the second row of the LM logs134 explained with FIG. 20, “20 (%)” is stored as “lowering rate offrequency of assignment” and “17920 (MiB)” is stored as “generated dirtypage.” Thus, the LM predicting unit 118 subtracts “0.2 (20 (%))” from“1” to work out “0.8” as the present frequency of assignment of the CPUto the target VM 3. Thereafter, the LM predicting unit 118 divides“17920 (MiB)” by “32 (s)” and “0.8” to work out “700 (MiB/s)” as thegeneration frequency of the dirty pages in the case in which thefrequency of assignment of the CPU has not been lowered.

Then, the LM predicting unit 118 divides the data amount of the data forwhich the transfer has been completed in the processing of S101 by thetime taken for the transfer of the data for which the transfer has beencompleted in the processing of S101 to calculate the transfer rate ofdata in the target VM 3 for which the re-execution has been started inthe processing of S51 (S104).

For example, on the second row of the LM logs 134 explained with FIG.20, “32000 (MiB)” is stored as “transferred data.” Thus, the LMpredicting unit 118 divides “32000 (MiB)” by “32 (s)” to work out “1000(MiB/s)” as the transfer rate of data.

Subsequently, the LM predicting unit 118 divides the data amount of thedirty pages identified in the processing of S102 by the transfer ratecalculated in the processing of S104 to calculate (infer) the time takenfor transfer of the dirty pages identified in the processing of S102(S105).

For example, the LM predicting unit 118 divides “1000 (MiB/s)” by “17920(MiB)” to work out “17.92 (s)” as the time taken for transfer of thedirty pages.

Thereafter, as illustrated in FIG. 15, the LM predicting unit 118determines whether or not the time calculated in the processing of S105or S114 is equal to or shorter than a given time (S111).

For example, if “17.92 (s)” is worked out as the time taken for transferof the dirty pages in the processing of S105 and the given time is “0.3(s),” the LM predicting unit 118 determines that the time calculated inthe processing of S105 is not equal to or shorter than the given time.

Then, if it is determined that the time calculated in the processing ofS105 or S114 is not equal to or shorter than the given time (NO ofS111), the LM predicting unit 118 calculates (infers) the dirty pagesupdated again after the transfer in the dirty pages identified in theprocessing of S102 by multiplying the generation frequency of the dirtypages calculated in the processing of S103, the time calculated in theprocessing of S105 or S114, and the frequency of assignment of the CPUwhen transfer of the dirty pages is carried out next (S113).

For example, when the generation frequency of the dirty pages calculatedin the processing of S103 is “700 (MiB/s)” and the time calculated inthe processing of S105 is “17.92 (s)” and the frequency of assignment ofthe CPU when transfer of the dirty pages is carried out next is “0.7,”the LM predicting unit 118 works out “8781 (MiB)” as the data amount ofthe dirty pages updated again after the transfer in the dirty pagesidentified in the processing of S102.

Then, the LM predicting unit 118 divides the data amount of the dirtypages calculated in the processing of S113 by the transfer rate of datacalculated in the processing of S104 to calculate the time taken fortransfer of the dirty pages calculated in the processing of S113 (S114).

For example, when the data amount of the dirty pages calculated in theprocessing of S113 is “8781 (MiB)” and the transfer rate of datacalculated in the processing of S104 is “1000 (MiB/s),” the LMpredicting unit 118 works out “8.781 (s)” as the time taken for transferof the dirty pages calculated in the processing of S113.

For example, the information management unit 111 may store the LM logs134 acquired in the processing of S52 in the information storage area130 after adding information that represents the data amount of thedirty pages calculated in the processing of S113 and the time calculatedin the processing of S114 (hereinafter, referred to also as inferenceinformation 134 a) to the LM logs 134. In the following, a descriptionwill be made about a specific example of the LM logs 134 in which theinference information 134 a is included about the case in which thesecond round of data transfer is carried out.

FIG. 21 and FIG. 22 are diagrams explaining specific examples of the LMlogs 134 in which the inference information 134 a is included. Forexample, FIG. 21 is a diagram explaining the specific example of the LMlogs 134 in which the inference information 134 a is included about thecase in which the second round of data transfer is carried out.

On the second row of the LM logs 134 explained with FIG. 20, “17920(MiB)” is stored as “generated dirty page.” Thus, if “1000 (MiB/s)” hasbeen worked out as the transfer rate of data, the LM predicting unit 118divides “17920 (MiB)” by “1000 (MiB/s)” to work out “(about) 18 (s).”Furthermore, on the second row of the LM logs 134 explained with FIG.20, “10:11:32” is stored as “clock time.” Thus, as illustrated in FIG.21, the LM predicting unit 118 stores “10:11:50” that is the clock timeobtained by adding “18 (s)” to “10:11:32” in “clock time” on the thirdrow of the LM logs 134, for example.

Moreover, as illustrated in FIG. 21, for example, the LM predicting unit118 stores “Iter 2” as “status” on the third row of the LM logs 134 andstores “30 (%)” as “lowering rate of frequency of assignment.” Inaddition, the LM predicting unit 118 stores “17920 (MiB)” as“transferred data” and stores “8781 (MiB)” as “generated dirty page.”

Referring back to FIG. 15, the LM predicting unit 118 executes theprocessing of S111 and the subsequent processing again after theprocessing of S114. Then, if it is determined that the time calculatedin the processing of S105 or S114 is equal to or shorter than the giventime (YES of S111), the LM predicting unit 118 calculates the rate bywhich the frequency of assignment of the CPU is desired to be loweredfor completing the live migration for which the re-execution has beenstarted in the processing of S51 from a combination of the dirty pagescalculated in the processing of S113 and the time calculated in theprocessing of S114, or a combination of the dirty pages identified inthe processing of S102 and the time calculated in the processing of S105(S112). In the following, a description will be made about a specificexample of the LM logs 134 in which the inference information 134 a isincluded about the case in which the seventh round of data transfer iscarried out.

FIG. 22 is a diagram explaining the specific example of the LM logs 134in which the inference information 134 a is included about the case inwhich the seventh round of data transfer is carried out. As illustratedin FIG. 22, the LM predicting unit 118 adds the new inferenceinformation 134 a that represents the data amount of the dirty pagescalculated in the processing of S113 and the time calculated in theprocessing of S114 to the LM logs 134 every time the processing of S113and S114 is executed similarly to the case explained with FIG. 21.

Here, on the eighth row of the LM logs 134 illustrated in FIG. 22, “11(MiB)” is stored in “generated dirty page.” Thus, if “1000 (MiB/s)” hasbeen worked out as the transfer rate of data, the LM predicting unit 118divides “11 (MiB)” by “1000 (MiB/s)” to work out “11 (ms).” Therefore,for example, if the given time in the processing of S111 is “30 (ms),”the LM predicting unit 118 determines that the time calculated in theprocessing of S114 is equal to or shorter than the given time.

Moreover, on the eighth row of the LM logs 134 illustrated in FIG. 22,“80 (%)” is stored as “lowering rate of frequency of assignment.” Thus,in this case, the LM predicting unit 118 works out “80 (%)” as the rateby which the frequency of assignment of the CPU is desired to be loweredfor completing the live migration for which the re-execution has beenstarted in the processing of S51.

Referring back to FIG. 13, the migration executing unit 112 determineswhether or not to cease the LM for which the re-execution has beenstarted in the processing of S51 based on the rate calculated in theprocessing of S46 and the rate calculated in the processing of S53(S54).

For example, on the eighth row of the LM logs 134 illustrated in FIG.22, “80 (%)” is set as “lowering rate of frequency of assignment.” Forexample, the LM logs 134 illustrated in FIG. 22 indicate that thefrequency of assignment of the CPU to the target VM 3 is desired to belowered by 80 (%) for completing the live migration for which there-execution has been started in the processing of S51.

Thus, for example, if the rate calculated in the processing of S46 is“94 (%),” the LM predicting unit 118 determines that the rate calculatedin the processing of S46 is higher than the rate calculated in theprocessing of S53. Therefore, in this case, the LM predicting unit 118determines that there is no need to cease the live migration for whichthe re-execution has been started in the processing of S51, for example.

Thereafter, the migration executing unit 112 outputs the determinationresult in the processing of S54 (S55). Specific examples of thedetermination result output in the processing of S55 will be describedbelow.

FIG. 23 and FIG. 24 are diagrams explaining the specific examples whenthe determination result output in the processing of S55 is output tooutput apparatus (not illustrated). In the examples illustrated in FIG.23 and FIG. 24, it is indicated that “response time (normal)” thatrepresents the response time calculated in the processing of S41 is “18(ms)” and “timeout of response time” that represents the responsepermitted time is “300 (ms)” and “permissible slowdown” that is the ratecalculated in the processing of S46 is “94 (%).”

Furthermore, in the example illustrated in FIG. 23, it is indicated that“Iteration” that represents the number of times of transfer of dataactually carried out is “1 (time)” and “slowdown” that represents therate of lowering of the frequency of assignment of the CPU actuallycarried out is “20 (%).” In addition, it is indicated that “transferreddata” that represents the data amount of data actually transferred is“32000 (MiB)” and “generated dirty page” that represents the data amountof the dirty pages actually generated is “17920 (MiB).”

Moreover, in the example illustrated in FIG. 23, it is indicated that“predicted Iteration” that represents the number of times of transfer ofdata desired in order for the transfer time of data to be equal to orshorter than the given time in the processing of S111 is “7 (times)” and“predicted slowdown” that represents the rate by which the frequency ofassignment of the CPU is desired to be lowered in order to cause thetransfer time of data to be equal to or shorter than the given time inthe processing of S111 is “80 (%).” In addition, it is indicated that“status” that represents the determination result about whether or notthere is a need to cease the live migration for which re-execution hasbeen started in the processing of S51 is “LM is continued because it isanticipated that influence on business does not occur.”

On the other hand, in the example illustrated in FIG. 24, it isindicated that “Iteration” that represents the number of times transferof data has been actually carried out is “1 (time)” and “slowdown” thatrepresents the rate of lowering of the frequency of assignment of theCPU actually carried out is “20 (%).” In addition, it is indicated that“transferred data” that represents the data amount of data actuallytransferred is “32000 (MiB)” and “generated dirty page” that representsthe data amount of the dirty pages generated when data is actuallytransferred is “28160 (MiB).”

Moreover, in the example illustrated in FIG. 24, it is indicated that“predicted Iteration” that represents the number of times of transfer ofdata desired in order for the transfer time of data to be equal to orshorter than the given time in the processing of S111 is “9 (times)” and“predicted slowdown” that represents the rate by which the frequency ofassignment of the CPU is desired to be lowered in order to cause thetransfer time of data to be equal to or shorter than the given time inthe processing of S111 is “99 (%).” In addition, it is indicated that“status” that represents the determination result about whether or notthere is a need to cease the live migration for which re-execution hasbeen started in the processing of S51 is “LM is suspended because it isanticipated that influence on business occurs.”

As above, if execution of live migration of the target VM 3 is notcompleted in the given time, the management VM 3 f in the presentembodiment acquires the first communication logs 132 about the target VM3. Then, the management VM 3 f calculates the first response time of aprocessing request to the target VM 3 based on the acquired firstcommunication logs 132.

Subsequently, the management VM 3 f lowers the frequency of assignmentof the CPU to the target VM 3 and thereafter acquires the secondcommunication logs 132 about the target VM 3. Then, the management VM 3f calculates the second response time of a processing request to thetarget VM 3 based on the acquired second communication logs 132.

Thereafter, the management VM 3 f calculates the first rate by which thefrequency of assignment may be lowered based on the rate by which thefrequency of assignment has been lowered, the first response time, thesecond response time, and the time permitted as the response time of aprocessing request to the target VM 3. Then, after start of re-executionof the live migration of the target VM 3, the management VM 3 fcalculates the second rate by which the frequency of assignment isdesired to be lowered for completing the re-execution based on theprogress status of the re-execution. Moreover, the management VM 3 fdetermines whether or not to cease the re-execution of the livemigration based on the first rate and the second rate.

For example, the management VM 3 f carries out slowdown of the target VM3 in a range in which the response time with respect to a processingrequest from a user does not exceed the permitted time and suppressesthe frequency of update of data in execution of the live migration ofthe target VM 3.

This allows the management VM 3 f to complete the live migration of thetarget VM 3 while keeping the services provided to users by the cloudcomputing user from being affected. For example, it becomes possible forthe management VM 3 f to complete the live migration of the target VM 3while suppressing the occurrence of an error and so forth associatedwith delay in the response time of a processing request from a user tothe target VM 3.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although one or more embodiments of thepresent invention have been described in detail, it should be understoodthat the various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

What is claimed is:
 1. A migration control apparatus comprising: amemory; and a processor coupled to the memory and the processorconfigured to acquire a first communication log regarding a firstvirtual machine when execution of migration of the first virtual machineis not completed in a specific time, perform, based on the acquiredfirst communication logs, calculation of a first response time of afirst processing request to the first virtual machine, acquire a secondcommunication log regarding the first virtual machine after loweringfrequency of assignment of a computing resource to the first virtualmachine to a first rate, perform, based on the acquired secondcommunication log, calculation of a second response time of a secondprocessing request to the first virtual machine, calculate, based on thefirst response time, the second response time, the first rate, and anallowable range of a response time, a second rate to which the frequencyof assignment of the computing resource to the first virtual machine ispermitted to be lowered, after start of re-execution of the migration ofthe first virtual machine, perform, based on a progress status of there-execution, calculation of a third rate of the frequency of assignmentof the computing resource for completing the re-execution, and perform,based on the second rate and the third rate, determination of whether tocease the re-execution.
 2. The migration control apparatus according toclaim 1, wherein the calculation of a first response time includescalculating, as the first response time, a time from a clock time when asession used for communication of the first processing request to thefirst virtual machine starts to a clock time when the session ends. 3.The migration control apparatus according to claim 1, wherein thecalculation of the second response time includes calculating, as thesecond response time, a time from a clock time when a session used forcommunication of the second processing request to the first virtualmachine starts to a clock time when the session ends.
 4. The migrationcontrol apparatus according to claim 1, wherein the calculation of thethird rate includes after transferring first data in the re-execution,identifying, from the first data, second data updated after thetransferring of the first data, calculating generation frequency of thesecond data by dividing a data amount of the identified second data by atime taken for the transferring of the first data and a rate of thefrequency of assignment of the computing resource, and calculating thethird rate based on the data amount of the second data and thecalculated generation frequency of the second data.
 5. The migrationcontrol apparatus according to claim 4, wherein the calculation of thethird rate includes calculating a transfer rate of data by dividing adata amount of the first data by the time taken for the transferring ofthe first data, calculating a time taken for transferring of the seconddata by dividing the data amount of the second data by the transferrate, and in a case where the calculated time taken for the transferringof the second data is not longer than a threshold, determining, as thethird rate, the rate of the frequency of assignment of the computingresource when the transferring of the first data is performed.
 6. Themigration control apparatus according to claim 5, wherein thecalculation of the third rate includes in a case where the calculatedtime taken for the transferring of the second data is longer than thethreshold, estimating a data amount of third data updated after thetransferring of the second data by multiplying the calculated time takenfor the transferring of the second data, the calculated generationfrequency of the second data, and the frequency of assignment of thecomputing resource when the transferring of the second data isperformed, calculating a time taken for transferring of the third databy dividing the estimated data amount of the third data by the transferrate, and in a case where the calculated time taken for the transferringof the third data is not longer than the threshold, determining, as thethird rate, the rate of the frequency of assignment of the computingresource when the transferring of the second data is performed.
 7. Themigration control apparatus according to claim 6, wherein thecalculation of the third rate includes when the calculated time takenfor the transferring of the third data is longer than the threshold,repeatedly executing processing of estimating a data amount andcalculating a time taken for transferring while changing a rate of thefrequency of assignment of the computing resource until a time taken fortransferring of data updated after transferring becomes equal to orshorter than the threshold.
 8. The migration control apparatus accordingto claim 6, wherein the frequency of assignment of the computingresource when the transferring of the second data is performed is lowerthan the frequency of assignment of the computing resource when thetransferring of the first data is performed, and the frequency ofassignment of the computing resource when the transferring of the thirddata is performed is lower than the frequency of assignment of thecomputing resource when the transferring of the second data isperformed.
 9. The migration control apparatus according to claim 1,wherein the determination of whether or not to cease the re-executionincludes determining to cease the re-execution of the migration when thethird rate is lower than the second rate.
 10. A computer-implementedmigration control method comprising: acquiring a first communication logregarding a first virtual machine when execution of migration of thefirst virtual machine is not completed in a specific time; calculating,based on the acquired first communication logs, a first response time ofa first processing request to the first virtual machine; acquiring asecond communication log regarding the first virtual machine afterlowering frequency of assignment of a computing resource to the firstvirtual machine to a first rate; calculating, based on the acquiredsecond communication log, a second response time of a second processingrequest to the first virtual machine; calculating, based on the firstresponse time, the second response time, the first rate, and anallowable range of a response time, a second rate to which the frequencyof assignment of the computing resource to the first virtual machine ispermitted to be lowered; after start of re-execution of the migration ofthe first virtual machine, calculating, based on a progress status ofthe re-execution, a third rate of the frequency of assignment of thecomputing resource for completing the re-execution; and determining,based on the second rate and the third rate, whether to cease there-execution.
 11. The migration control method according to claim 10,wherein the calculating of a first response time includes calculating,as the first response time, a time from a clock time when a session usedfor communication of the first processing request to the first virtualmachine starts to a clock time when the session ends.
 12. The migrationcontrol method according to claim 10, wherein the calculating of thesecond response time includes calculating, as the second response time,a time from a clock time when a session used for communication of thesecond processing request to the first virtual machine starts to a clocktime when the session ends.
 13. The migration control method accordingto claim 10, wherein the calculating of the third rate includes aftertransferring first data in the re-execution, identifying, from the firstdata, second data updated after the transferring of the first data,calculating generation frequency of the second data by dividing a dataamount of the identified second data by a time taken for thetransferring of the first data and a rate of the frequency of assignmentof the computing resource, and calculating the third rate based on thedata amount of the second data and the calculated generation frequencyof the second data.
 14. The migration control method according to claim13, wherein the calculating of the third rate includes calculating atransfer rate of data by dividing a data amount of the first data by thetime taken for the transferring of the first data, calculating a timetaken for transferring of the second data by dividing the data amount ofthe second data by the transfer rate, and in a case where the calculatedtime taken for the transferring of the second data is not longer than athreshold, determining, as the third rate, the rate of the frequency ofassignment of the computing resource when the transferring of the firstdata is performed.
 15. The migration control method according to claim14, wherein the calculating of the third rate includes in a case wherethe calculated time taken for the transferring of the second data islonger than the threshold, estimating a data amount of third dataupdated after the transferring of the second data by multiplying thecalculated time taken for the transferring of the second data, thecalculated generation frequency of the second data, and the frequency ofassignment of the computing resource when the transferring of the seconddata is performed, calculating a time taken for transferring of thethird data by dividing the estimated data amount of the third data bythe transfer rate, and in a case where the calculated time taken for thetransferring of the third data is not longer than the threshold,determining, as the third rate, the rate of the frequency of assignmentof the computing resource when the transferring of the second data isperformed.
 16. The migration control method according to claim 15,wherein the calculating of the third rate includes when the calculatedtime taken for the transferring of the third data is longer than thethreshold, repeatedly executing processing of estimating a data amountand calculating a time taken for transferring while changing a rate ofthe frequency of assignment of the computing resource until a time takenfor transferring of data updated after transferring becomes equal to orshorter than the threshold.
 17. The migration control method accordingto claim 15, wherein the frequency of assignment of the computingresource when the transferring of the second data is performed is lowerthan the frequency of assignment of the computing resource when thetransferring of the first data is performed, and the frequency ofassignment of the computing resource when the transferring of the thirddata is performed is lower than the frequency of assignment of thecomputing resource when the transferring of the second data isperformed.
 18. The migration control method according to claim 10,wherein the determining of whether or not to cease the re-executionincludes determining to cease the re-execution of the migration when thethird rate is lower than the second rate.
 19. A non-transitorycomputer-readable medium storing instructions executable by one or morecomputers, the instructions comprising: one or more instructions foracquiring a first communication log regarding a first virtual machinewhen execution of migration of the first virtual machine is notcompleted in a specific time; one or more instructions for calculating,based on the acquired first communication logs, a first response time ofa first processing request to the first virtual machine; one or moreinstructions for acquiring a second communication log regarding thefirst virtual machine after lowering frequency of assignment of acomputing resource to the first virtual machine to a first rate; one ormore instructions for calculating, based on the acquired secondcommunication log, a second response time of a second processing requestto the first virtual machine; one or more instructions for calculating,based on the first response time, the second response time, the firstrate, and an allowable range of a response time, a second rate to whichthe frequency of assignment of the computing resource to the firstvirtual machine is permitted to be lowered; one or more instructionsfor, after start of re-execution of the migration of the first virtualmachine, calculating, based on a progress status of the re-execution, athird rate of the frequency of assignment of the computing resource forcompleting the re-execution; and one or more instructions fordetermining, based on the second rate and the third rate, whether tocease the re-execution.